{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import operator\n",
    "\n",
    "def createDateSet():\n",
    "    group = np.array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1]])\n",
    "    labels = ['A', 'A', 'B', 'B']\n",
    "    return group, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.   1.1]\n",
      " [ 1.   1. ]\n",
      " [ 0.   0. ]\n",
      " [ 0.   0.1]]\n",
      "(4, 2)\n",
      "[[ 0.5  1. ]\n",
      " [ 0.5  1. ]\n",
      " [ 0.5  1. ]\n",
      " [ 0.5  1. ]]\n",
      "[[ 0.25  1.  ]\n",
      " [ 0.25  1.  ]\n",
      " [ 0.25  1.  ]\n",
      " [ 0.25  1.  ]]\n",
      "[ 1.5  1.5  1.5  1.5]\n",
      "[[ 0.71886544  0.88569899  0.86848132  0.90097703]\n",
      " [ 0.24455199  0.62748657  0.08674429  0.76095836]\n",
      " [ 0.28306329  0.0293541   0.37527946  0.79496708]]\n",
      "[ 3.37402277  1.71974122  1.48266393]\n",
      "[2 1 0]\n"
     ]
    }
   ],
   "source": [
    "group, labels = createDateSet()\n",
    "print(group)\n",
    "print(group.shape)\n",
    "diff = np.tile([0.5,1], (4,1))\n",
    "print(diff) # 将0.5重复(4,1)次，第二个参数相当于数组的shape，第一个参数是数组元素的值\n",
    "print(diff**2)\n",
    "print(diff.sum(axis=1))\n",
    "b = np.random.random((3,4))\n",
    "print(b)\n",
    "#print(b.sum(axis=0)) #每列的和\n",
    "distances = b.sum(axis=1)\n",
    "print(distances) #每行的和\n",
    "print(distances.argsort())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k):\n",
    "    # 距离计算, inX是一个要分类的向量\n",
    "    dataSetSize = dataSet.shape[0]\n",
    "    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet #目标向量与dataSet相减，每一行就是Ax-Bx, Ay-By\n",
    "    sqDiffMat = diffMat**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1) #每行的和\n",
    "    distances = sqDistances**0.5  # inX向量与dataSet里每个向量的距离\n",
    "    sortedDistIndicies = distances.argsort() # 把索引号按它的值从小到大排列\n",
    "    \n",
    "    classCount = {} #key是label，value是该类的数量\n",
    "    for i in range(k): #取距离最小的前k个值，对它们的label出现的次数进行累加\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1\n",
    "    #将classCount字典分解为元组列表，然后使用operator运算符模块的itemgetter方法，按照第二个元素的次序对元组进行排序\n",
    "    sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n",
    "    # sortedClassCount的结果类似[('B', 2), ('A', 1)]\n",
    "    return sortedClassCount[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B\n"
     ]
    }
   ],
   "source": [
    "group, labels = createDateSet()\n",
    "print(classify0([0,0], group, labels, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def file2matrix(filename):\n",
    "    # 将文本文件的数据转成矩阵，每一行有四个数据，分别是飞行里程，玩视频游戏的百分比，每周消费冰激凌，以及标签\n",
    "    fr = open(filename)\n",
    "    arrayOfLines = fr.readlines()\n",
    "    numOfLines = len(arrayOfLines)\n",
    "    resultMat = np.zeros((numOfLines, 3)) #生成一个shape为(n,3)的二维数组\n",
    "    labels = []\n",
    "    index = 0\n",
    "    for line in arrayOfLines:\n",
    "        line = line.strip() # 截掉回车字符\n",
    "        listFromLine = line.split('\\t')\n",
    "        resultMat[index, :] = listFromLine[0:3]\n",
    "        labels.append(int(listFromLine[-1]))\n",
    "        index += 1\n",
    "    return resultMat, labels\n",
    "\n",
    "# 数值归一化 newValue = (oldValue - min)/(max - min)\n",
    "def normalize(dataSet):\n",
    "    minVal = dataSet.min(0) #按列取最小值\n",
    "    maxVal = dataSet.max(0)\n",
    "    ranges = maxVal - minVal\n",
    "    m = dataSet.shape[0]\n",
    "    normDataSet = dataSet - np.tile(minVal, (m,1))\n",
    "    normDataSet = normDataSet/np.tile(ranges, (m,1))\n",
    "    return normDataSet, ranges\n",
    "\n",
    "def datingClassTest():\n",
    "    # 随机选取10%的数据作为测试数据集\n",
    "    hoRatio = 0.1\n",
    "    datingDataMat, labels = file2matrix(\"datingTestSet2.txt\")\n",
    "    normData, ranges = normalize(datingDataMat)\n",
    "    m = np.shape(normData)[0]\n",
    "    numOfTestVec = int(m*hoRatio) #取前10%的数据作为测试数据集\n",
    "    errorCount = 0\n",
    "    for i in range(numOfTestVec):\n",
    "        classifyResult = classify0(normData[i], normData[numOfTestVec:m], labels[numOfTestVec:m], 3)\n",
    "        if (classifyResult != labels[i]):\n",
    "            print(\"classify result %d, real answer is %d\" % (classifyResult, labels[i]))\n",
    "            errorCount+=1\n",
    "    print(\"total error rate is %f\" % (errorCount/float(numOfTestVec)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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lm07qQ4cOMXXqVM6fP4/H42HixIm4XMWx7TxY1kEqVw5ky5ZNZM+enUGDBjFhwjd4PC+Q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jS8E1SXyYSOLDpBJtok/GoOpV0p6nYV/Gvhix701MmzaNkJAQxo8fxvjxwwkNDeWTTz5BkiS+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\nDY1mGTY2Be/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c3T2JibHizQBBknbi42PC9etXf/PcgQMHsX37UXS60QjNExVJ2oyPj83v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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11090b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "datingDataMat, labels = file2matrix(\"datingTestSet2.txt\")\n",
    "normData, ranges = normalize(datingDataMat)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111) #subplot()指定numrows, numcols以及fignum\n",
    "# 取数组的第2个和第3个元素显示在坐标轴上, 散点图\n",
    "ax.scatter(normData[:, 0], normData[:, 1], 15.0*np.array(labels), 15.0*np.array(labels)) \n",
    "#plt.xlabel('game')\n",
    "#plt.ylabel('ice cream')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify result 1, real answer is 2\n",
      "classify result 3, real answer is 1\n",
      "classify result 3, real answer is 1\n",
      "classify result 2, real answer is 3\n",
      "classify result 3, real answer is 1\n",
      "total error rate is 0.050000\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify result 7, real answer is 1\n",
      "classify result 9, real answer is 3\n",
      "classify result 8, real answer is 3\n",
      "classify result 2, real answer is 3\n",
      "classify result 9, real answer is 5\n",
      "classify result 3, real answer is 5\n",
      "classify result 4, real answer is 5\n",
      "classify result 6, real answer is 8\n",
      "classify result 2, real answer is 8\n",
      "classify result 3, real answer is 8\n",
      "classify result 1, real answer is 8\n",
      "classify result 1, real answer is 8\n",
      "classify result 5, real answer is 9\n",
      "total error rate is 0.013742\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "# 识别手写数字，每个手写数字都被处理成32*32的数字\n",
    "def trainingMatrix(dirname):\n",
    "    '''\n",
    "    训练数据是一个n*1024的二维数组\n",
    "    '''\n",
    "    trainingFileList = os.listdir(dirname) # 列出目录下的文件名\n",
    "    labels = []\n",
    "    numOfFiles = len(trainingFileList)\n",
    "    trainingMat = np.zeros((numOfFiles, 1024))\n",
    "    for i in range(numOfFiles):\n",
    "        filename = trainingFileList[i]\n",
    "        trainingMat[i] = fileToVector(os.path.join(dirname, filename))\n",
    "        classNum = int(filename.split(\"_\")[0])\n",
    "        labels.append(classNum)\n",
    "    return trainingMat, labels\n",
    "\n",
    "def fileToVector(filename):\n",
    "    '''\n",
    "    将一个手写数字的文件处理成1024的向量，shape为(1, 1024)的numpy数组\n",
    "    '''\n",
    "    returnVec = np.zeros((1, 1024))\n",
    "    fr = open(filename)\n",
    "    for i in range(32):\n",
    "        l = fr.readline()\n",
    "        for j in range(32):\n",
    "            returnVec[0, 32*i+j] = int(l[j])\n",
    "    return returnVec\n",
    "\n",
    "def writingClassTest():\n",
    "    # 用测试集检查分类效果\n",
    "    trainingMat, trainingLabels = trainingMatrix(\"trainingDigits\")\n",
    "    testingFileList = os.listdir(\"testDigits\")\n",
    "    errorCount=0\n",
    "    for filename in testingFileList:\n",
    "        testingVec = fileToVector(os.path.join(\"testDigits\", filename)) #测试集里每个文件是一个待分类向量\n",
    "        classNum = int(filename.split(\"_\")[0])\n",
    "        classifyResult = classify0(testingVec, trainingMat, trainingLabels, 3)\n",
    "        if (classifyResult != classNum):\n",
    "            print(\"classify result %d, real answer is %d\" % (classifyResult, classNum))\n",
    "            errorCount+=1\n",
    "    print(\"total error rate is %f\" % (errorCount/float(len(testingFileList))))\n",
    "\n",
    "writingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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